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---
task_categories:
- object-detection
license: wtfpl
dataset_info:
- config_name: With augmentation
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: category
dtype: string
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num_examples: 8037
- name: validation
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num_examples: 100
- name: test
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num_examples: 20
download_size: 590150250
dataset_size: 2864026
- config_name: Without augmentation
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
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- name: id
dtype: int64
- name: area
dtype: int64
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num_examples: 2659
- name: validation
num_bytes: 37647
num_examples: 100
- name: test
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num_examples: 18
download_size: 512953012
dataset_size: 977453
---
# AnimeHeadsv3 Object Detection Dataset
The AnimeHeadsv3 Object Detection Dataset is a collection of anime and art images, including manga pages, that have been annotated with object bounding boxes for use in object detection tasks.
## Contents
There are two versions of the dataset available:
The dataset contains a total of 8157 images, split into training, validation, and testing sets. The images were collected from various sources and include a variety of anime and art styles, including manga.
- Dataset with augmentation: Contains 8157 images.
- Dataset without augmentation: Contains 2777 images.
The images were collected from various sources and include a variety of anime and art styles, including manga. The annotations were created using the COCO format, with each annotation file containing the bounding box coordinates and label for each object in the corresponding image. The dataset has only one class named "head".
## Preprocessing
The dataset with augmentation has the following preprocessing parameters:
Resize: Fit within 640x640
The dataset without augmentation does not have any preprocessing applied.
## Augmentation Parameters
The following augmentation parameters were applied to the dataset with augmentation:
Outputs per training example: 3
Flip: Horizontal
Saturation: Between -40% and +40%
Blur: Up to 4px
Noise: Up to 4% of pixels
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